In 2016 OpenAI released Gym, a software toolkit for reinforcement learning research, described in the paper “OpenAI Gym” by Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba, posted to arXiv on June 5, 2016. Gym packaged a growing collection of benchmark problems behind a single common interface and provided a website for sharing and comparing results.
Before Gym, reinforcement learning research was fragmented. Every group wrote its own environments and its own glue code, which made results hard to reproduce and harder to compare. Gym standardized the agent-environment loop into a small, consistent API: the environment hands the agent an observation, the agent returns an action, and the environment returns the next observation and a reward. That simple contract let a researcher swap one algorithm for another, or one environment for another, without rewriting everything.
Gym shipped with environments ranging from classic control problems and Atari games to robotics tasks built on physics simulators, and it quickly became the de facto standard for benchmarking RL algorithms. The interface outlived OpenAI’s own stewardship: maintenance later passed to the community as Gymnasium, but the original Gym design remains the template the field still uses.